We present an empirical ambiguity analysis method based on a finite number of acceptable solutions that are representative of the ambiguity region. These solutions are submitted to a Q-mode factor analysis that indicates which parameters are ambiguous and their ambiguity range. We illustrate, with a synthetic nonlinear example, that our method is more effective than singular value decomposition analysis in producing an average trend of the ambiguity region. It requires less restrictive hypotheses and is more robust than analytical methods of ambiguity analysis, in the sense of being applicable to a broader class of problems.

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